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All Journal Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Scientific Journal of Informatics
Wicaksono, Rizky Dwi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering

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KLASIFIKASI GENDER BERDASARKAN CITRA MATA MANUSIA MENGGUNAKAN ALGORITMA CONVOLUTION NEURAL NETWORK Wicaksono, Rizky Dwi; Pratama, Fandy Indra; Budianita, Avira
Prosiding Sains Nasional dan Teknologi Vol. 14 No. 1 (2024): Seminar Nasional Sains dan Teknologi 14
Publisher : Fakultas Teknik Universitas Wahid Hasyim

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36499/psnst.v14i1.12035

Abstract

Teknologi otentikasi biometrik yang memanfaatkan karakteristik manusia seperti wajah, sidik jari, suara, dan iris mata semakin banyak digunakan untuk seperti wajah, sidik jari, suara, dan iris mata semakin banyak digunakan untuk identifikasi individu. Meskipun efektif dalam memastikan keaslian, sistem-sistem ini umumnya tidak memberikan informasi tambahan seperti jenis kelamin atau etnis dari individu yang diverifikasi. Penelitian sebelumnya telah meneliti klasifikasi jenis kelamin berdasarkan citra wajah, sedangkan penggunaan citra iris mata masih terbatas. Penelitian ini bertujuan untuk mengklasifikasikan jenis kelamin manusia berdasarkan citra iris mata dengan menerapkan pendekatan deep learning, khususnya menggunakan algoritma Convolutional Neural Network (CNN). Pengujian dilakukan pada sejumlah besar data untuk mengukur kinerja. Hasil penelitian menunjukkan bahwa model CNN mampu mencapai akurasi hingga 92% dalam mengklasifikasikan jenis kelamin berdasarkan citra iris mata. Penelitian ini membuka peluang baru untuk pengembangan lebih lanjut dalam biometrik berbasis iris mata.
Comparison of KNN and CNN Algorithms for Gender Classification Based on Eye Images Wicaksono, Rizky Dwi; Fajar Shidiq, Guruh
Scientific Journal of Informatics Vol. 11 No. 4: November 2024
Publisher : Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/sji.v11i4.13529

Abstract

Purpose: This study explores gender classification using iris images and compares two methods k-nearest neighbors (KNN) and convolutional neural networks (CNN). Most research has focused on facial recognition. However, iris classification is more unique and accurate. This research addresses a gap in gender classification using iris images. It also tests the effectiveness of CNN and KNN for this task. Methods: This study used 11,525 iris images from Kaggle. Of these, 6,323 were male and 5,202 were female. The authors split the data into training (75%) and testing (25%). Preprocessing involved normalizing and augmenting images by rotating, scaling, shifting, and reflecting the them. Pixel values were also adjusted. The study compared the KNN algorithm, using Euclidean distance and 16 neighbors, with a CNN model. The CNN had layers for convolution, pooling, and density. The authors performed evaluation using accuracy, precision, recall, F1-score, and confusion matrix. Result: The KNN model demonstrated 81% accuracy. It identified males with 87% precision but only 70% recall. Meanwhile, the CNN model was better, achieving 93% accuracy with 94% precision and 95% recall for males. The CNN model outperformed KNN for females in precision, recall, and F1-score, indicating its superior ability to learn patterns and classify gender from iris images. Novelty: CNN outperforms KNN in classifying gender from iris images. It effectively recognizes patterns and achieves high accuracy. The study shows CNN’s superiority in biometric tasks, suggesting that future research should balance datasets and test better models, as well as combining models for better performance.
Co-Authors Avira Budianita Fajar Shidiq, Guruh Pratama, Fandy Indra